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A multifaceted approach to understanding the botnet phenomenon

Abu Rajab, Moheeb, Zarfoss, Jay, Monrose, Fabian, Terzis, Andreas·2006·Proceedings of the 6th ACM SIGCOMM conference on Internet measurement

Abstract

The academic community has long acknowledged the existence of malicious botnets, however to date, very little is known about the behavior of these distributed computing platforms. To the best of our knowledge, botnet behavior has never been methodically studied, botnet prevalence on the Internet is mostly a mystery, and the botnet life cycle has yet to be modeled. Uncertainty abounds. In this paper, we attempt to clear the fog surrounding botnets by constructing a multifaceted and distributed measurement infrastructure. Throughout a period of more than three months, we used this infrastructure to track 192 unique IRC botnets of size ranging from a few hundred to several thousand infected end-hosts. Our results show that botnets represent a major contributor to unwanted Internet traffic - 27% of all malicious connection attempts observed from our distributed darknet can be directly attributed to botnet-related spreading activity. Furthermore, we discovered evidence of botnet infections in 11% of the 800,000 DNS domains we examined, indicating a high diversity among botnet victims. Taken as a whole, these results not only highlight the prominence of botnets, but also provide deep insights that may facilitate further research to curtail this phenomenon.

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Metadata

Authors
Abu Rajab, M., Zarfoss, J., Monrose, F., & Terzis, A.
Venue
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Publisher
ACM
Published
2006-10-25
Pages
41-52
OpenAlex
W2102671922
Cited by
595
References
0
FWCI
38.00
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: A multifaceted approach to understanding the botnet phenomenon. https://app.cochat.ai/papers/10.1145/1177080.1177086
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Abu Rajab, M., Zarfoss, J., Monrose, F., & Terzis, A. (2006). A multifaceted approach to understanding the botnet phenomenon. Proceedings of the 6th ACM SIGCOMM conference on Internet measurement. https://doi.org/10.1145/1177080.1177086

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.